Multi-culture Facial Attractiveness Enhancement Based on Double Knowledge Transferring

This paper presents a novel double knowledge transferring method to solve the multi-culture facial attractiveness enhancement problem. The existing enhancement of facial attractiveness methods just focus on one particular culture and assume the beautification model learned in one culture could be used in other cultures without adaptation. However, for the people in different cultures who do not share the same characteristics, existing models do not perform well. To address this problem, we employ the double knowledge transferring method to reduce data diversity. First, to reduce the aesthetic differences among cultures, we apply regressive manifold regularization to train a rating transferring function; second, to reduce the facial geometry feature differences, we use latent MLT (Multi-Task Learning) algorithm to find appropriate feature mappings. Treat Chinese as source culture, we apply the proposed algorithm to Malay people and demonstrate its effectiveness. Keyword: Facial attractiveness, transfer learning, manifold regularization.